Mining operations can be modelled as a supply chain from the sources of raw material (mines) through various processing streams to the final saleable products sold to customers. Existing research in mine design optimization has predominantly focused on the extraction sequence of materials and neglects the impact that selecting the optimal processing paths has on the economic viability of a mining operation. Additionally, the vast majority of existing models assume that the value of the extracted material must be calculated for each block individually, and neglects the opportunity to blend material together to prolong the life of the resource and generate higher cash flows. One major contributing factor for not modelling such complexities is the difficulty in modelling and optimizing with non-linear relationships that occur in the supply chain's processing streams.

This paper addresses the issue of selecting the optimal block destinations and processing streams throughout a mining supply chain for complex blending operations. This non-linear optimization formulation is solved using a particle swarm optimization algorithm. The k-means clustering technique is used to aggregate the processing decision variables and substantially reduce the size of the problem, leading to more efficient solution times with minimal loss in the quality of the resultant solution. By clustering the decision variables and applying the same decisions over a set of geological simulations, the proposed method also provides complex destination policies for material extracted from a mine under geological uncertainty, somewhat akin to a more advanced cut-off policy. This method is tested at Vale's Onça Puma nickel laterite deposit, located in Pará, Brazil.